Method for optimizing the quality of network resources and the number of services likely to use said resources

ABSTRACT

The invention relates to a method for optimizing the quantity of network resources and the number of services likely to use said resources in a virtualized telecommunications network comprising the following steps of:
         evaluating similarities between the services likely to use said resources in terms of the virtual network functions VNFs required for the instantiation of each service,   gathering services as a function of their similarities in order to maximize resource sharing,   calculating additional resources to accept services awaiting instantiation;   running an admission control scheme to accept or reject requests for the instantiation of new services.

TECHNICAL FIELD

The invention is in the field of virtualized telecommunication networksand more specifically relates to a method for optimizing the quantity ofnetwork resources and the number of services likely to use saidresources.

The invention also relates to a computer program stored on a recordingmedium and containing instructions for implementing the method when runon a computer.

STATE OF PRIOR ART

Over the last decade, the technological revolution that characterizesmobile telephone networks has changed the way we communicate and led tonew applications and services, which will characterize the communicationsystems fifth generation (5G) and subsequent developments.

These new applications and services are gathered into three case of useclasses, each characterized by specific requirements and Key PerformanceIndicators (KPIs):

-   -   Ultra-reliable, low-latency communications (URLLC),    -   Massive machine-type communications (mMTC),    -   Enhanced Mobile Broadband (eMBB).

FIG. 1 illustrates the correspondence between these cases of use andrequirements associated therewith. To effectively support cases of useand applications with heterogeneous requirements, 5G and latercommunication systems will instantiate a new and flexible architecturein which the network infrastructure is logically divided into differentinstances, i.e. network slices, each designed for a specific servicerunning in the cloud environment. A network slice consists of PhysicalNetwork Functions (PNF) and Virtual Network Functions (VNF). A VNFrepresents the software implementation, i.e. operating in a cloudenvironment, of traditional functions such as routing or packetscheduling. The network slice concept is schematically illustrated inFIG. 2. The main standardization body for network slicing is the 3GPP(Third Generation Partnership Project). Its specifications detail thenetwork functionalities and their sequencing to provide these slices onthe Radio Access Network (RAN) and the Core Network (CN) [3GPPTS28.531].

In addition to designing the access and core functions, one of the mostdifficult tasks is to transition from a fixed-capacity OSS (OperatingSupport System) and BSS (Business Support System) system to a newhierarchy of elements that have to deal with a very complex ecosystem,both from the point of view of network users, network slices, andservices that generally have different throughput and QoS requirements.

In addition to management, future 5G and subsequent networks willrequire orchestration capabilities, which are divided into two maincategories, service orchestration and resource orchestration. Tasks suchas sharing a VNF between network slices, their location in a highlyheterogeneous cloud infrastructure or the number of allocated coreprocessors are just a few examples of the responsibilities of the M&O,Management and Orchestration, function.

Functions related to the lifecycle management of virtual networkfunctions VNFs and the orchestration of their resources go beyond theresponsibility of the 3GPP group. The standardization body in charge ofdefining these functions is the European Telecommunications StandardsInstitute (ETSI), more precisely the Network Function StandardizationGroup (NFV) [ETSIGR].

FIG. 3 shows the correspondence between 3GPP network slices and conceptsintroduced by ETSI NFV. A network slice is seen from ETSI's point ofview as a network service. FIG. 4 illustrates the correspondence betweennetwork slices and required network functions. A network function can bededicated to a specific slice or shared between several slices. FIG. 5schematically illustrates shared network functions and dedicated networkfunctions within a network slice.

According to the 3GPP group, a network slice is a logic network thatextends over the Radio access network RAN and the core network CN. Tomanage each domain, the 3GPP group suggests to break down a slice intoRAN and CN slice subnets and defines the Network Slice ManagementFunction (NSMF) and the Network Slice Subnet Management Function(NSSMF). The NSMF is the entity responsible for managing a slice acrossmultiple domains and the NSSMF is the entity responsible for managingslice subnets in a specific domain [3GPPTS28.531]. A slice subnetcomprises radio and core network functionalities configured to provideparticular behaviour and meet specific requirements. To create andmanage a slice, the NSMF (and NSSMFs) use a set of predefineddescriptors or a template. Each descriptor, called Network SliceTemplate (NST), provides a description of the functionalities and theirspecific configuration (for example, operating frequency bands). Inparticular, it describes the list of NFV components that have to beinstantiated to configure a network slice instance, as well as otherinformation, such as configurations, life cycle steps, actions,monitoring, rules, service level agreements, etc.

However, these standardization frameworks only define guidelines onarchitectural aspects (interfaces and requirements) and designprinciples without providing specific solutions for the configurationand management of network slices, such as the allocation of thenecessary resources by the VNF, priority or quality of servicemanagement. Consequently, it is necessary to create new schemes andframeworks capable of managing network resources related to radio,transport and cloud domains. In addition, it is necessary to take intoaccount the fact that different slice requests may have differentpriorities and requirements and therefore the network resources have tobe managed to ensure cross resource orchestration and management.

The purpose of the invention is to optimize the management andorchestration of networks by avoiding both under-provisioning andover-dimensioning of resources, which are the main causes of serviceoutages and excessive expenses respectively for mobile communicationservice providers.

DISCLOSURE OF THE INVENTION

The invention is implemented by means of a cellular network managementand orchestration system configured to receive service instantiationrequests and to decide which network slices can be instantiated/acceptedto implement these services according to their types/priorities,resource needs, the presence of other simultaneous requests andavailable network capacities (which depend on the network slices alreadyinstantiated).

The objective of the invention is therefore to increase the efficiencyof the network, i.e. to increase the number of services instantiatedusing a given set of network resources, or equivalently, to limit thequantity of resources required to meet the requirements of a given setof services. This objective is achieved by means of a method foroptimizing the quantity of network resources and the number of serviceslikely to use said resources in a telecommunications network, comprisingthe following steps of:

-   -   evaluating similarities between the services likely to use said        resources in terms of the virtual network functions VNFs        required for the instantiation of each service,    -   group services into network slices according to their        similarities in order to maximize resource sharing,    -   calculating additional resources to accept services awaiting        instantiation,    -   running an admission control scheme to accept or reject requests        for the instantiation of new services.

By identifying and gathering services with common needs into networkslices, instead of creating a new slice for each requested service, themethod according to the invention makes it possible to reduce theresources consumed globally so as to use, if necessary, a single sliceto support services thus identified. In this way, the method accordingto the invention also makes it possible to reduce the time required toinstantiate a new network service.

According to the invention, each service is instantiated in a logicnetwork slice (NSI) comprising at least two sets of virtual networkfunctions, i.e. a set of network functions K^(R) which correspond to theradio access network RAN and a set of network functions K^(C) whichcorrespond to the core network.

The method according to the invention further comprises the steps of:

-   -   defining a set        =        ^(R)∪        ^(C)=f NF₁, . . . ,NF_(K)) representing all the network        functions K=(K_(R)+K_(C)) likely to be instantiated in the        telecommunications network, and,    -   for a given logical service n awaiting instantiation, defining a        list D_(n) containing the network functions required for the        instantiation of this service and a description of the        interactions between said network functions and parameters        describing their respective configurations,    -   defining a set of instantiated network slices comprising network        functions shared between at least two network services and        network functions specifically dedicated to a network service,    -   comparing the network functions required by a network service        awaiting instantiation with those of all the NSIs likely to        serve the service awaiting instantiation with a minimum of        additional network resources,    -   evaluating the similarities between the network functions being        compared, and,    -   based on the similarities evaluated, selecting the NSI that        shares the greatest number of network functions with the service        awaiting instantiation,    -   if no NSI shares network functions with the service awaiting        instantiation, creating a new NSI, consisting exclusively of        network functions specifically dedicated to the service awaiting        instantiation.

In addition, for each service awaiting instantiation, n∈

={0,1, . . . ,N}, where N is an integer N≥0, is defined a subset D_(n)of the set

=

^(R)∪

^(C)={NF₁, . . . ,NF_(K)} containing the network functions required forits instantiation and a set of configuration parameters

_(n,k) for each network function, and, to indicate the network functionsthat make up the nth request D_(n), the vector λ_(n)∈{0,1}^(K) isdefined, whose k-th input is defined by:

$\lambda_{n,k} = \left\{ {\begin{matrix}{1,} & {{{if}\mspace{14mu} {NF}_{k}} \in _{n}} \\{0,} & {else}\end{matrix},{\forall{{NF_{k}} \in .}}} \right.$

Then, for each network function NF_(k) belonging to D_(n), theconfiguration parameters associated therewith are represented as a setof J binary vectors as follows:

_(n,k) ={l _(n,k,1) ,l _(n,k,2) , . . . ,l _(n,k,J)}.

Then the configuration parameters are mapped with a parameter d_(n,k,r)representing the quantity of communication resources, a parameterd_(n,k,c) representing the computing resources of the calculation, and aparameter d_(n,k,m) representing the cloud storage capacity required bythe network function NF_(k) of the n-th service instantiation requestusing a template consisting of a static and a dynamic part:

d _(n,k,r)=ρ_(k) +f _(k,r)(

_(n,k)),

d _(n,k,c)=χ_(k) f _(k,c)(

_(n,k)),

d _(n,k,m)+μ_(k) +f _(k,m)(

_(n,k)),

where ρ_(k), χ_(k) and μ_(k) represent the minimum quantity of resourcesrequired to activate a given network function NF_(k) ∈D_(n), andf_(k,r)(

_(n,k)), f_(k,c) (

_(n,k)) and f_(k,m)(

_(n,k)) representing the additional quantity of resources required,which depends on the configuration parameters

_(n,k), of the network function NF_(k),and the total request for communication resources, computing resourcesand cloud storage resources for said n-th request is calculated by thefollowing formula:

T _(n)=(d _(n,r) ,d _(n,c) ,d _(n,m)),

where d_(n,r)=

d_(n,k,r), d_(n,c)=

d_(n,k,c) and d_(n,m)=

d_(n,k,m)).

Evaluation of similarities between the network functions required by anetwork slice awaiting instantiation and those of the set of NSIs

already instantiated is obtained by calculating a Jaccard similarityparameter by the following formula:

${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{{\lambda_{n}} + {\lambda_{i}} - {\lambda_{n}\lambda_{i}}}},{i \in},$

where λ_(i) indicates the network functions that make up the NSI i∈

already instantiated, and ∥⋅∥ representing a Euclidean norm operator.

If none of the NSIs already instantiated is adapted to the serviceawaiting instantiation, a new NSI is defined.

On the basis of the similarities calculated, the already instantiatedNSI i*∈

is selected, which shares the largest number of network functions VNFswith the network service awaiting instantiation n:

$i^{*} = {\underset{i \Subset \mathcal{B}}{{argmax}\;}{\Lambda_{i,n}.}}$

Then, n is temporarily added to the list of services related to NSI i*.

According to the invention, for each pair of network services n,n′∈R_(i*,k), where R_(i*,k) represents all the services included in theNSI i*∈

which require the network function NF_(k) ∈

_(n),the similarity between a first set of configuration parameters

_(n,k) and a second set of configuration parameters

_(n′,k) is defined as follows:

${{C\left( {\mathcal{L}_{n,k},\mathcal{L}_{n^{\prime},k}} \right)} = {\sum\limits_{j = 1}^{J}{h\left( {l_{n,k,j},l_{n^{\prime},k,j}} \right)}}},$

where J is the number of parameters of the network function NF_(k), andh(⋅) is the cosine similarity function (or cosine metric) which allowscalculation of the similarity between two N-dimensional vectors bydetermining the cosine of the angle between them:

${h\left( {l_{n,k,j},l_{n^{\prime},k,j}} \right)} = {\frac{l_{n,k,j}l_{n^{\prime},k,j}}{{l_{n,k,j}}{l_{n^{\prime},k,j}}}.}$

And, for each network function NF_(k) ∈

_(n) of the service n, the quantity of resources that can be pooledbetween the different network services instantiated in the NSI i* andthe new service n through the parameter σ*_(n,k,j) is evaluated asfollows:

${\sigma_{n,k,j}^{*} = {\max\limits_{n^{\prime} \neq {n\; \epsilon \; _{i,k}}}{C\left( {\mathcal{L}_{n,k},\mathcal{L}_{n^{\prime},k}} \right)}}},{i \in},$

where the index j represents either communication resources, computingresources or storage resources.

According to another characteristic of the invention, the resourcesrequired to instantiate each function of the service n are calculatedvia the NSI i* using a template consisting of a static and a dynamicpart:

d′ _(n,k,r)=ρ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,r)(

_(n,k)),

d′ _(n,k,c)=χ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,c)(

_(n,k)),

d′ _(n,k,m)=μ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,m)(

_(n,k)),

where β(x) is the characteristic function, which is equal to 1 if x=0and is equal to 0 if x≠0 and, the total request for communicationresources, computing resources and cloud storage resources for said nthservice instantiation request is calculated as follows:

T′ _(n)=(d _(n,r) ,d _(n,c) ,d _(n,m)),

where d′_(n,r)=

d′_(n,k,r),d′_(n,c)=

d′_(n,k,c) and d′_(n,m)=

d′_(n,k,m).

In one embodiment of the invention, the services previously instantiatedin the set of NSIs are periodically re-classified using a normalizedspectral classification algorithm, the services being represented asnodes of a connected graph and clusters are found by partitioning thisgraph according to their spectral decomposition into sub-graphs.

In addition, the set

={1, N_(R)) of services already instantiated with NF requests {λ₁, . . ., A_(N) _(R) } is defined and an affinity matrix

which has the element

_(n,n′) describes the Jaccard similarity between two alreadyinstantiated services n,n′∈

is periodically calculated as follows:

n , n ′ = { Λ n , n ′ , n ≠ n ′ 0 , else ,

Where Δ_(n,n′), is calculated by

${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{{\lambda_{n}} + {\lambda_{i}} - {\lambda_{n}\lambda_{i}}}},{i \in .}$

Then from

are deduced the corresponding diagonal matrix S whose element (n, n) isthe sum of the n-th row of A, s_(n,n)=

Δ_(n,n′) and the associated normalized Laplacian matrix as:

ℒ = S - 1 2  - 1 2 ,

Then the normalized eigenvectors of

are calculated and the first k eigenvectors are gathered using K-means,the number of clusters k is obtained such that all eigenvalues (1, . . ., k) are very small, but the (k+1)-th is relatively large.

According to the invention, requests for the instantiation of newnetwork services are ordered as a function of:

-   -   their waiting time in a queue and then, from the oldest one,        requests that can be fulfilled according to their request for        resources are admitted; or    -   the average quantity of resources requested, then it is checked        which requests can be fulfilled, starting from the request        characterized by the smallest request for network resources.

BRIEF DESCRIPTION OF THE DRAWINGS

Further characteristics and advantages of the invention will appear fromthe following description, taken as a non-limiting example, withreference to the appended figures in which:

FIG. 1 illustrates the correspondence between new applications andservices and the requirements associated with these new applications andservices in 5G communication systems,

FIG. 2 schematically illustrates the network slice concept,

FIG. 3 illustrates the correspondence between 3GPP network slices andthe concepts of ETSI NFV (European Telecommunication Standard Institute,Network Functions Virtualization),

FIG. 4 shows the correspondence of network services with the requirednetwork functions,

FIG. 5 represents an illustration of shared network functions anddedicated network functions in a network slice instance (NSI) thatenables the simultaneous implementation of multiple services,

FIG. 6 represents a first flowchart illustrating essential steps of themethod according to the invention,

FIG. 7 represents a second flowchart illustrating steps in a particularembodiment of the invention.

DETAILED DISCLOSURE OF SPECIFIC EMBODIMENTS

The method according to the invention will be described within the scopeof a cellular network management and orchestration system configured toreceive service instantiation requests and to decide to accept or refusethese services according to their types/priorities, resourcerequirements, the presence of other simultaneous requests and availablenetwork capacities (which depend on the network slices alreadyinstantiated).

For the sake of clarity, in the following description, the instantiationof a network slice will mean the instantiation of the applications andservices likely to be provided in that slice.

It should be remembered that each service is instantiated in a logicnetwork slice comprising at least two sets of virtual network functions(VNF), i.e. a set of network functions K^(R) which correspond to theradio access network RAN and a set of network functions K^(C)corresponding to the core network.

The implementation of the method according to the invention will bedescribed in reference to FIG. 6. Beforehand, a set of VNFs

=

^(R)∪

^(C)={NF₁, . . . , NF_(K)} representing all the network functionsK=(K^(R)+K^(C)) likely to be instantiated in the telecommunicationsnetwork is defined, and, for a given network service, a subset D_(n) ofthe set

containing the network functions VNFs required for its instantiation anda description of the interactions between said network functions andparameters describing their respective configurations are defined. Then,to indicate the network functions VNFs required for the instantiation ofthe service n∈

, in step 4 is introduced the vector λ_(n)∈{0,1}^(K), the k-th input ofwhich is defined by:

$\lambda_{n,k} = \left\{ {\begin{matrix}{1,} & {{{if}\mspace{14mu} {NF}_{k}} \in _{n}} \\{0,} & {else}\end{matrix},{\forall{{NF_{k}} \in .}}} \right.$

The set

of NSIs instantiated is then defined, and, for each element of this seti∈

, the subset of the set

containing the network functions VNFs instantiated for the NSI i. As foreach network service, for each NSI i∈

, the vector λ_(i) is defined to indicate the available networkfunctions VNFs.

A Jaccard similarity parameter is then calculated in step 6 to performevaluation of the similarities between the network functions required bya service awaiting instantiation. n with those of all NSIs

already instantiated.

The Jaccard similarity parameter is defined by the following formula:

${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{{\lambda_{n}} + {\lambda_{i}} - {\lambda_{n}\lambda_{i}}}},{i \in},$

In step 8, it is checked whether there is i∈

, such that Λ_(i,n)>0.

If yes, on the basis of the similarities calculated, in step 10, the NSIalready instantiated i* ∈

which shares the largest number of network functions VNFs with thenetwork service awaiting instantiation n is selected:

$i^{*} = {\underset{i\; \epsilon \; \mathcal{B}}{{argmax}\;}{\Lambda_{i,n}.}}$

After temporarily adding n to the list of services associated with NSIi*∈

, for each pair of services n, n′ belonging to R_(i*,k) where R_(i*,k)represents the set of services included in the NSI i*which require thenetwork function N F_(k) ∈

_(n), in step 12, the similarity between the configuration parameters ofthe two services

_(n,k) and

_(n′, k) is defined as follows:

${{C\left( {\mathcal{L}_{n,k},\mathcal{L}_{n^{\prime},k}} \right)} = {\sum\limits_{j = 1}^{J}{h\left( {l_{n,k,j},l_{n^{\prime},k,j}} \right)}}},$

where J is the number of parameters of the network function NF_(k).

In step 14, the resources needed to be able to instantiate the service nthrough the selected NSI i* are determined, and in step 16 it isevaluated whether the resource request associated with this new servicen can be accepted.

If there is no i∈

such that Λ_(i,n)>0, i.e. if none of the NSIs of

shares network functions with the network service awaiting instantiationn, in step 18, a new NSI is defined and its request for resources iscalculated, and then the admission control process is started todetermine whether the service can be instantiated (step 16).

In one particular embodiment of the invention illustrated in FIG. 7, theservices previously instantiated in the set of NSIs are periodicallyre-classified via a normalized spectral classification algorithm, theservices being represented as nodes of a connected graph and clustersare found by partitioning this graph according to their spectraldecomposition into sub-graphs.

To this end, the set

={1, N_(R)) of services already instantiated with requests NF {λ₁, . . ., λ_(N) _(R) } is defined and in step 20, an affinity matrix

, the element

_(n,n′) of which is the Jaccard similarity between two services n,n′∈

already instantiated, is periodically calculated as follows:

n , n ′ = { Λ n , n ′ , n ≠ n ′ 0 , else ,

where Λ_(n,n′) is calculated by

${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{\left. ||\lambda_{n}||{+ \left. ||\lambda_{i}||{{- \lambda_{n}}\lambda_{i}} \right.} \right.}},{i \in .}$

Then in step 22, from

are deduced the corresponding diagonal matrix S the element (n, n) ofwhich is the sum of the n-th row of A, s_(n,n)=

Λ_(n,n′) and the associated normalized Laplacian matrix as:

ℒ = S - 1 2  - 1 2 ,

Then, in step 24, the normalized eigenvectors of

are calculated and the first k eigenvectors are gathered using K-means,the number of clusters k is obtained such that all the eigenvalues (1, .. . , k) are very small, but the (k+1)-th one is relatively large. Othersimilarity calculation techniques can be alternatively used as describedin the papers entitled “Content caching clustering based on piecewiseinterest similarity,” in Proc. IEEE GLOBECOM, December 2017, pp. 1-6 and“Cache-aided coded multicast for correlated sources,” in Proc. of 9thInternational Symposium on Turbo Codes and Iterative InformationProcessing (ISTC), September 2016, pp. 360-364.

Preferably, requests for the instantiation of new network services areordered according to their waiting time in a queue, and then, based onthe oldest one, the NSI capable of instantiating this service and theassociated quantity of network resources are determined, and thenrequests that can be fulfilled according to their request for resourcesare admitted.

In addition, requests for instantiation of new network services areranked in relation to the average quantity of resources requested, andthen checked to see which requests can be met, starting with the requestcharacterized by the lowest request for network resources.

In one first alternative implementation, in the admission control stepfor accepting or rejecting requests for instantiation of new services,requests are classified into M classes, each characterized by a gainrelated to the instantiation acceptance r_(m) and an instantiationrejection cost I_(m), m E M. In addition, each network service requestis stored in a queue with a size Q_(m) dedicated to its class. A newservice request is deleted when the corresponding queue is full. Themanagement and orchestration systems perceive a gain related toinstantiation acceptance r_(n), when a request for a slice of the classm is accepted and an instantiation refusal cost I_(m) when the requestis deleted from its queue. Therefore, at time t, the action a_(m)(t) ofthe admission controller can be evaluated through a reward function asfollows:

R(t) = a_(m)(t)r_(m) − d_(m)(t)l_(m),

where d_(m) (t) indicates the number of requests for service of theclass m abandoned at time t, which can be calculated as follows:

d _(m)(t)=max{s _(m)(t)−a _(m)(t)+n _(m)(t)−Q _(m),0},

where s_(m) (t) and n_(m) (t) indicate respectively the number ofrequests for slice of the mth class in the queue and the additionalslice requests received at time t.

A first solution is to maximize R (t) at each time interval t.

A second approach is to maximize a long-term reward function as follows:

$\overset{\_}{R} = {\sum\limits_{t = 0}^{\infty}\; {\gamma^{t}{R(t)}}}$

where 0<γ<1 is a parameter called a reduction factor, which determinesthe importance of actions at each time interval on the long-termevaluation function.

Reinforcement learning can be used to optimize the function R=Σ_(t=0)^(∞)γ^(t) R (t). There are many algorithms available for this purpose,such as Q-learning.

In a second alternative of the admission control step for accepting orrejecting requests for instantiation of new services, as in the firstalternative, service requests are classified into M classes, eachcharacterized by a gain related to the instantiation acceptance r_(m)and potentially an instantiation refusal cost I_(m), m∈M. Then, acontroller momentarily reduces the quantity of resources for some of theservices already instantiated, to potentially increase the number ofservices that can be accepted at a given slot. For example, thecontroller reduces the resources allocated to lower priority services inorder to instantiate higher priority services. This limits the number ofabandoned requests for low priority services. As a result, a smartcontroller can maximize a reward that takes into account the cost ofreducing the resources allocated to services instantiated with lowpriority. In this case, the action a_(m)(t) of the admission controllercan be evaluated as follows:

R(t) = a_(m)(t)r_(m) − d_(m)(t)l_(m) − n_(cc, m)(t)l_(cc, m)

where n_(cc,m)(t) is the quantity of resources reduced to the servicem∈M and I_(cc,m) is the cost associated with this action. As in theprevious embodiment, it is possible to simply maximize R(t) at each timeslice. On the other hand, reinforcement learning algorithms could beused to optimize the long-term instantiation acceptance P.

1: A method of optimizing the quantity of network resources and thenumber of services likely to use said resources in a virtualizedtelecommunications network characterized by the following steps of:evaluating the similarities between the services likely to use saidresources in terms of the virtual network functions (VNF) required forthe instantiation of each service, gathering services into networkslices according to their similarities in order to maximize resourcesharing, calculating additional resources to accept services awaitinginstantiation, and, running an admission control scheme to accept orreject requests for the instantiation of new services. 2: The methodaccording to claim 1, wherein each service is instantiated in a logicnetwork slice (NSI) comprising at least two sets of virtual networkfunctions, namely a set of network functions K^(R) which correspond tothe radio access network RAN and a set of network functions K^(C) whichcorrespond to the core network. 3: The method according to claim 2further comprising the steps of: defining a set

=

∪

^(C)={NF₁, . . . ,NF_(K)} representing the set of network functionsK=(K_(R)+K_(C)) likely to be instantiated in the telecommunicationsnetwork, and, for a given logic service n awaiting instantiation,defining a list D_(n) containing the network functions required for theinstantiation of this service and a description of the interactionsbetween said network functions and parameters describing theirrespective configurations, defining a set of instantiated network slicescomprising network functions shared between at least two networkservices and network functions specifically dedicated to a networkservice, comparing the network functions required by the network serviceawaiting instantiation with those of the set of NSIs likely to serve theservice awaiting instantiation with a minimum of additional networkresources, evaluating the similarities between the network functionsbeing compared, and, based on the similarities evaluated, selecting theNSI that shares the greatest number of network functions with theservice awaiting instantiation, if no NSI shares network functions withthe service awaiting instantiation, creating a new NSI, consistingexclusively of network functions specifically dedicated to the serviceawaiting instantiation. 4: The method according to claim 3, wherein, foreach service awaiting instantiation, n∈

={0,1, . . . ,N}, N representing an integer N≥0, is defined a subsetD_(n) of the set

=

^(R)∪

^(C)={NF₁, . . . ,NF_(K)} containing the network functions required forits instantiation and a set of configuration parameters

_(n,k) for each network function, and, to indicate the network functionsthat make up the n-th request D_(n), is defined (4) the vectorλ_(n)∈{0,1}^(K), the k-th input of which is defined by:λ_(n, k) = {, ∀NF_(k) ∈ . then, for each network function NF_(k)belonging to D_(n), the associated configuration parameters arerepresented as a set of J binary vectors as follows:

_(n,k) ={l _(n,k,1) ,l _(n,k,2) , . . . ,l _(n,k,J)} then theconfiguration parameters are mapped with a parameter d_(n,k,r)representing the quantity of communication resources, a parameterd_(n,k,c) representing the computing resources of the calculation, and aparameter d_(n,k,m) representing the cloud storage capacity required bythe network function NF_(k) of the n-th service instantiation requestusing a template consisting of a static and a dynamic part:d _(n,k,r)=ρ_(k) +f _(k,r)(

_(n,k)),d _(n,k,c)=χ_(k) f _(k,c)(

_(n,k)),d _(n,k,m)=μ_(k) +f _(k,m)(

_(n,k)), where ρ_(k), χ_(k) and μ_(k) represent the minimum quantity ofresources required to activate a given network function NF_(k) ∈D_(n),and f_(k,r)(

_(n,k)), f_(k,c)(

_(n,k)), and f_(k,m)(

_(n,k)) representing the additional quantity of resources required,which depends on the configuration parameters.

_(n,k) of the network function NF_(k), and the total request forcommunication resources, computing resources and cloud storage resourcesfor said n-th request is calculated by the following formula:T _(n)=(d _(n,r) ,d _(n,c) ,d _(n,m)) where d_(n,r)=

d_(n,k,r), d_(n,c)=

d_(n,k,c) and d_(n,m)=

d_(n,k,m). 5: The method according to claim 4, wherein evaluating thesimilarities between the network functions required by a network sliceawaiting instantiation and those of the set of NSIs

already instantiated is obtained by calculating a Jaccard similarityparameter by the following formula:${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{\left. ||\lambda_{n}||{+ \left. ||\lambda_{i}||{{- \lambda_{n}}\lambda_{i}} \right.} \right.}},{i \in},$where λ_(i) indicates the network functions that make up the NSI i∈

already instantiated, and ∥⋅∥ representing a Euclidean norm operator. 6:The method according to claim 5, wherein, if none of the NSIs alreadyinstantiated is adapted to the service awaiting instantiation, a new NSIis defined (18). 7: The method according to claim 5, wherein, on thebasis of the similarities calculated, is selected (10) the alreadyinstantiated NSI i*∈

which shares the largest number of network functions VNFs with thenetwork service awaiting instantiation n:$i^{*} = \underset{i \in \mathcal{B}}{{argmax\Lambda}_{i,n}.}$ then, nis temporarily added to the list of services related to NSI i*. 8: Themethod according to claim 7, wherein, for each pair of network servicesn, n′∈R_(i*,k), where R_(i*,k) the set of services included in the NSIi*∈

which require the network function NF_(k) ϵ

_(n), the similarity between a first set of configuration parameters

_(n,k) and a second set of configuration parameters

_(n′,k) is defined as follows:${{C\left( {\mathcal{L}_{n,k},\mathcal{L}_{n^{\prime},k}} \right)} = {\sum\limits_{j = 1}^{J}\; {h\left( {l_{n,k,j},l_{n^{\prime},k,j}} \right)}}},$where J is the number of parameters of the network function NF_(k) andh(⋅) is the cosine similarity function (or cosine metric) which allowscalculation of the similarity between two N-dimensional vectors bydetermining the cosine of the angle between them:${h\left( {l_{n,k,j},l_{n^{\prime},k,j}} \right)} = {\frac{l_{n,k,l}l_{n^{\prime},k,j}}{\left. ||l_{n,k,j}||||l_{n^{\prime},k,j} \right.||}.}$9: The method according to claim 8, wherein for each network functionNF_(k) ∈

_(n) of the service n, the quantity of resources that can be pooledbetween the different network services instantiated in the NSI i* andthe new service n through the parameter σ*_(n,k,j) are evaluated asfollows:${\sigma_{n,k,j}^{*} = {\max\limits_{{n^{\prime} \neq n} \in _{i,k}}{C\left( {\mathcal{L}_{n,k},\mathcal{L}_{n^{\prime},k}} \right)}}},{i \in},$where the index j represents either communication resources, computingresources or storage resources. 10: The method according to claim 9,wherein the resources required to instantiate each function of theservice n are calculated via the NSI i* using a template consisting of astatic and a dynamic part:d′ _(n,k,r)=ρ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,r)(

_(n,k)),d′ _(n,k,c)=χ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,c)(

_(n,k)),d′ _(n,k,m)=μ_(k)β(σ*_(n,k,j))+(1−σ*_(n,k,j))f _(k,m)(

_(n,k)), where β(x) is the characteristic function, which is equal to 1if x=0 and is equal to 0 if x≠0 and, the total request for communicationresources, computing resources and cloud storage resources for said n-thservice instantiation request is calculated as follows:T′ _(n)=(d′ _(n,r) d′ _(n,c) d′ _(n,m)), where d′_(n,r)=

d′_(n,k,r),d′_(n,c)=

d′_(n,k,c) and d′_(n,m)=

d′_(n,k,m). 11: The method according to claim 3, wherein the servicespreviously instantiated in the set of NSIs

are periodically re-classified, via a normalized spectral classificationalgorithm, the services being represented as nodes of a connected graphand clusters are found by partitioning this graph according to theirspectral decomposition into subgraphs. 12: The method according to claim11, wherein the set R={1, N_(R)) of the services already instantiated isdefined with requests NF {λ₁, . . . , A_(N) _(R) } and an affinitymatrix

, the elements of which are the Jaccard similarity between two alreadyinstantiated services n,n′∈

is periodically calculated (20) as follows: n , n ′ = { Λ n , n ′ , n ≠n ′ 0 , else , where Λ_(n,n′) is calculated by${\Lambda_{i,n} = \frac{\lambda_{n}\lambda_{i}}{\left. ||\lambda_{n}||{+ \left. ||\lambda_{i}||{{- \lambda_{n}}\lambda_{i}} \right.} \right.}},{i \in},$then (22) is deduced from

the corresponding diagonal matrix S the element (n, n) of which is thesum of the n-th row of A, s_(n,n)=

Λ_(n,n′), and the associated normalized Laplacian matrix as: ℒ = S - 1 2 - 1 2 , and then, the normalized eigenvectors of

are calculated (24) and the first k eigenvectors are gathered usingK-means, the number of clusters k is obtained such that all theeigenvalues (1, . . . , k) are very small, but the (k+1)-th isrelatively large. 13: The method according to claim 10, wherein requestsfor the instantiation of new network services are ordered as a functionof: their waiting time in a queue and then, from the oldest one,requests that can be fulfilled as a function of their request forresources are admitted; or the average quantity of resources requested,then which requests can be fulfilled are checked, starting from therequest characterized by the smallest request for network resources. 14:A computer program stored on a recording medium and containinginstructions for implementing the method according to claim 1 when runon a computer.